Bioethics最新文献

Geroscience and philosophy of aging have tended to focus their analyses on the biological and chronological dimensions of aging. Namely, one ages with the passage of time and by experiencing the cellular-molecular deterioration that accompanies this process. However, our concept of aging depends decisively on the social valuations held about it. In this article, we will argue that, if we study social aging in the contemporary world, a novel phenomenon can be identified: the paradox of aging in the digital era. If the social understanding of aging today is linked to unproductivity and obsolescence; then there is a possibility that, given the pace of change of digital technologies, we become obsolete at an early chronological and biological age, and therefore, feel old at a younger age. First, we will present the social dimension of aging based on Rowe and Kahn's model of successful aging. We will also show that their notion of social aging hardly considers structural aspects and weakens their approach. Second, departing from social aging in its structural sense, we will develop the paradox of aging in the digital era. On the one hand, we will explain how the institutionalization of aging has occurred in modern societies and how it is anchored in the concepts of obsolescence and productivity. On the other hand, we will state the kind of obsolescence that digitalization produces and argue that it can make cohorts of biologically and chronologically young individuals obsolete, and thus they would be personally and socially perceived as old.

Although self-testing apps, a form of mobile health (mHealth) apps, are often marketed as empowering, it is not obvious how exactly they can empower their users—and in which sense of the word. In this article, I discuss two conceptualisations of empowerment as polar opposites—one in health promotion/mHealth and one in feminist theory—and demonstrate how both their applications to individually used self-testing apps run into problems. The first, prevalent in health promotion and mHealth, focuses on internal states and understands empowerment as an individual process. However, this version of empowerment has been accused of paternalism and responsibilisation. The second, feminist version considers structural conditions and foregrounds collective, political change, whose realisation is not obviously attainable for an individually used app. By pointing out the flaws of the positions that focus on either internal states or external conditions, and by engaging with theory from critical phenomenology, I argue that the interplay between them is where empowerment can take place. I propose to formulate empowerment in phenomenological terms as a shift in being-in-the-world and discuss how this conceptualisation of empowerment would avoid the criticism of previous empowerment narratives while being realisable by self-testing apps.

The research we fund today will improve the health of people who will live tomorrow. But future people will not all benefit equally: decisions we make about what research to prioritize will predictably affect when and how much different people benefit from research. Organizations that fund health research should thus fairly account for the health needs of future populations when setting priorities. To this end, some research funders aim to allocate research resources in accordance with disease burden, prioritizing illnesses that cause more morbidity and mortality. In this article, I defend research funders' practice of aligning research funding with disease burden but argue that funders should aim to align research funding with future—rather than present—disease burden. I suggest that research funders should allocate research funding in proportion to aggregated estimates of disease burden over the period when research could plausibly start to yield benefits until indefinitely into the future.

Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well-represented populations. Faced with this dilemma between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well-represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms—showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings.

After a wave of breakthroughs in image-based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single-purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate. Focusing on issues like trust, transparency, threats of patient autonomy, responsibility issues in the collaboration of clinicians and ML models, fairness, and privacy, it will be argued that the main problems will be continuous with the current debate. However, due to functioning of large language models, the complexity of all these problems increases. In addition, the article discusses some profound challenges for the clinical evaluation of large language models and threats to the reproducibility and replicability of studies about large language models in medicine due to corporate interests.

Some authors argue that it is permissible for clinicians to conscientiously provide abortion services because clinicians are already allowed to conscientiously refuse to provide certain services. Call this the symmetry thesis. We argue that on either of the two main understandings of the aim of the medical profession—what we will call “pathocentric” and “interest-centric” views—conscientious refusal and conscientious provision are mutually exclusive. On pathocentric views, refusing to provide a service that takes away from a patient's health is professionally justified because there are compelling reasons, based on professional standards, to refuse to provide that service (e.g., it does not heal, and it is contrary to the goals of medicine). However, providing that same service is not professionally justified when providing that service would be contrary to the goals of medicine. Likewise, the thesis turns out false on interest-centric views. Refusing to provide a service is not professionally justified when that service helps the patient fulfill her autonomous preferences because there are compelling reasons, based on professional standards, to provide that service (e.g., it helps her achieve her autonomous preferences, and it would be contrary to the goals of medicine to deny her that service). However, refusing to provide that same service is not professionally justified when refusing to provide that service would be contrary to the goals of medicine. As a result, on either of the two most plausible views on the goals of medicine, the symmetry thesis turns out false.

Advances in assisted reproductive technologies can give rise to several ethical challenges. One of these challenges occurs when the reproductive desires of two individuals become incompatible and conflict. To address such conflicts, it is important to unbundle different aspects of (non)parenthood and to recognize the corresponding reproductive rights. This article starts on the premise that the six reproductive rights—the right (not) to be a gestational, genetic, and social parent—are negative rights that do not entail a right to assistance. Since terminating or continuing a pregnancy is a form of assistance, the right (not) to be a gestational parent should enjoy primacy in conflicts. However, while refusing assistance may hinder the reproductive project of another person, “prior assistance” does not entitle someone to violate a reproductive right. Therefore, our analysis provides reasons to argue that someone has a right to unilaterally use cryopreserved embryos or continue the development of an entity in an extracorporeal gestative environment (i.e., ectogestation). Although this could lead to a violation of the right not to be a genetic parent, it does not necessarily entail a violation of the right not to be a social parent.

This article argues for a ban on the performance of medically unnecessary genital normalizing surgeries as part of assigning a binary sex/gender to infants with intersex conditions on the basis of autonomy, regardless of etiology. It does this via a dis/analogy with the classic case in bioethics of Jehovah Witness (JW) parents' inability to refuse life-saving blood transfusions for their minor children. Both cases address ethical medical practice in situations where parents are making irreversible medical decisions on the basis of values strongly held, identity, and relationship-shaping values—such as religious beliefs or beliefs regarding the inherent value of binary sex/gender—amidst ethical pluralism. Furthermore, it takes seriously—as we must in the intersex case—that the restriction of parents' right to choose will likely result in serious harms to potentially large percentage of patients, their families, and their larger communities. I address the objection that parents' capacity to choose is restricted in the JW case on the basis of the harm principle or a duty to nonmaleficence, given that the result of parent choice would be death. I provide evidence that this is mistaken from how we treat epistemic uncertainty in the JW case and from cases in which clinicians are ethically obligated to restrict the autonomy of nonminor patients. I conclude that we restrict the parents' right to choose in the JW case—and should in the case of pediatric intersex surgery—to secure patient's future autonomy.

In a challenge trial, research subjects are purposefully exposed to some pathogen in a controlled setting, in order to test the efficacy of a vaccine or other experimental treatment. This is an example of medical effective altruism (MEA), where individuals volunteer to risk harms for the public good. Many bioethicists rejected challenge trials in the context of Covid-19 vaccine research on ethical grounds. After considering various grounds of this objection, I conclude that the crucial question is how much harm research subjects can permissibly risk. But we lack a satisfying way of making this judgment that does not appeal simply to the intuitions of doctors or bioethicists. I consider one recent and structurally plausible approach to critically evaluating the harm question. Alex London defends a social consistency test for research risks: we should compare the risks undertaken by research subjects to relevantly similar risks which are accepted in other spheres of society. I argue there is no good reason not to consider volunteer military service as a relevant social comparison. This implies there is essentially no cap on acceptable risks on the social consistency rationale. In short, if soldiers can be heroes, why can't research volunteers?